Altmetric

Analysis of protein-coding genetic variation in 60,706 humans

File Description SizeFormat 
nature19057.pdfPublished version1.85 MBAdobe PDFView/Open
Title: Analysis of protein-coding genetic variation in 60,706 humans
Authors: Lek, M
Karczewski, KJ
Minikel, EV
Samocha, KE
Banks, E
Fennell, T
O'Donnell-Luria, AH
Ware, JS
Hill, AJ
Cummings, BB
Tukiainen, T
Birnbaum, DP
Kosmicki, JA
Duncan, LE
Estrada, K
Zhao, F
Zou, J
Pierce-Hoffman, E
Berghout, J
Cooper, DN
Deflaux, N
DePristo, M
Do, R
Flannick, J
Fromer, M
Gauthier, L
Goldstein, J
Gupta, N
Howrigan, D
Kiezun, A
Kurki, MI
Moonshine, AL
Natarajan, P
Orozco, L
Peloso, GM
Poplin, R
Rivas, MA
Ruano-Rubio, V
Rose, SA
Ruderfer, DM
Shakir, K
Stenson, PD
Stevens, C
Thomas, BP
Tiao, G
Tusie-Luna, MT
Weisburd, B
Won, HH
Yu, D
Altshuler, DM
Ardissino, D
Boehnke, M
Danesh, J
Donnelly, S
Elosua, R
Florez, JC
Gabriel, SB
Getz, G
Glatt, SJ
Hultman, CM
Kathiresan, S
Laakso, M
McCarroll, S
McCarthy, MI
McGovern, D
McPherson, R
Neale, BM
Palotie, A
Purcell, SM
Saleheen, D
Scharf, JM
Sklar, P
Sullivan, PF
Tuomilehto, J
Tsuang, MT
Watkins, HC
Wilson, JG
Daly, MJ
MacArthur, DG
Exome Aggregation Consortium
Item Type: Journal Article
Abstract: Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human 'knockout' variants in protein-coding genes.
Issue Date: 17-Aug-2016
Date of Acceptance: 24-Jun-2016
URI: http://hdl.handle.net/10044/1/39541
DOI: http://dx.doi.org/10.1038/nature19057
ISSN: 0028-0836
Publisher: Nature Publishing Group
Start Page: 285
End Page: 291
Journal / Book Title: Nature
Volume: 536
Issue: 7616
Copyright Statement: This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons licence, users will need to obtain permission from the licence holder to reproduce the material. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Sponsor/Funder: The Academy of Medical Sciences
Wellcome Trust
Funder's Grant Number: WHCC_P48756
107469/Z/15/Z
Keywords: Exome Aggregation Consortium
General Science & Technology
MD Multidisciplinary
Publication Status: Published
Appears in Collections:National Heart and Lung Institute



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commons